Genome-Wide Association Study for Milk Protein Composition Traits in a Chinese Holstein Population Using a Single-Step Approach

Associations Between Polymorphisms of the CSN1S1, CSN1S2, CSN2 and CSN3 Genes and Milk Composition Traits in Holstein Cattle

BACKGROUND

Caseins are the major proteins found in cow's milk. There are four known casein fractions: αS1-casein, αS2-casein, β-casein and κ-casein, encoded by the CSN1S1, CSN1S2, CSN2 and CSN3 genes located on the sixth chromosome of cattle. These genes are considered candidate genes in marker-assisted selection. Therefore, it is essential to determine the relationship between these genes and quantitative characters.

OBJECTIVES

This study aimed to identify genotypes of CSN1S1, CSN1S2, CSN2 and CSN3 genes and investigate their effect on milk components with the PCR-RFLP method in Holstein cattle.

METHODS

The material of the study consisted of 519 Holstein cows that managed under intensive systems in Konya (n:189), Manisa (n:195) and Diyarbakır (n:135) provinces in Türkiye. Blood and milk samples from these cows were used in the study. The genetic structures of bovine CSN1S1, CSN1S2, CSN2 and CSN3 genes were examined by PCR-RFLP in three Holstein cattle populations. A general linear model (GLM) was applied to analyse the effect of genotypic variants on phenotypic characters.

RESULTS

Results indicated that milk solids-non-fat (SNF) (p < 0.01), protein (p < 0.05) and lactose (p < 0.01) percentages were significantly affected by the genetic variants of the CSN2 gene of cow in general population. CSN2 A2A2 genotype led to a significant increase in SNF, protein and lactose percentages by 0.14, 0.05 and 0.08 in comparison to other genotypes, respectively. Moreover, significant effect of the CSN1S1 BC (p < 0.05) and CSN3 AA (p < 0.01) genotypes on fat percentage were found in Konya province. Furthermore, a statistically significant genotype-by-environment interaction was identified in both the CSN1S1 (p < 0.05) and CSN3 (p < 0.01) genes in relation to milk fat content.

CONCLUSIONS

As a result, after increasing the number of studies that investigated the relationship between casein genes and milk traits and determined the genetic variation of CSN1S1, CSN2 and CSN3 genes of the Holstein cattle, these genes can be a strong genetic marker as marker-assisted selection programme in early selection.

Effects of hypoxia stress on the milk synthesis in bovine mammary epithelial cells

BACKGROUND

Milk synthesis is an energy-intensive process influenced by oxygen availability. This study investigates how hypoxia affects milk synthesis in BMECs, focusing on key genes involved in lactation and energy metabolism.

METHODS

BMECs were cultured in a normoxic environment and then transferred to a hypoxia chamber with 1% O2 for specified durations. The study evaluated cellular responses through various molecular experiments and RNA sequencing. Small interfering RNA was employed to knock down HIF-1α to investigate whether the lactation-related phenotype alteration depends on HIF-1α.

RESULTS

Hypoxia disrupted milk protein production by reducing mTOR/P70S6K/4EBP1 signaling and downregulating genes critical for amino acid transport and protein synthesis. Triglyceride synthesis increased due to enhanced fatty acid uptake and the upregulation of regulatory proteins, including FASN and PPARγ. Although glucose uptake was elevated under hypoxia, key enzymes for lactose synthesis were downregulated, suggesting a redirection of glucose toward energy production. Mitochondrial function was impaired under hypoxia, with reduced gene expression in TCA cycle, ETC, cytosol-mitochondrial transport, decreased ATP levels, increased ROS levels, and structural alterations. Additionally, lipid synthesis and glucose uptake depend on HIF-1α, while milk protein synthesis alterations occurred independently of HIF-1α.

CONCLUSIONS

Hypoxia alters milk synthesis in BMECs by disrupting milk protein synthesis, enhancing lipid metabolism, and impairing energy production. These findings provide valuable insights into the molecular mechanisms underlying the effect of oxygen deprivation on lactation efficiency, offering potential targets for mitigating hypoxic stress in the mammary glands of dairy animals.

Analysis of Population Structure and Selective Signatures for Milk Production Traits in Xinjiang Brown Cattle and Chinese Simmental Cattle

This study aims to elucidate the population structure and genetic diversity of Xinjiang brown cattle (XJBC) and Chinese Simmental cattle (CSC) while conducting genome-wide selective signatures analyses to identify selected genes associated with milk production traits in both breeds. Based on whole-genome resequencing technology, whole-genome single nucleotide polymorphisms (SNPs) of 83 Xinjiang brown cattle and 80 Chinese Simmental cattle were detected to resolve the genetic diversity and genetic structure of the two populations, whole-genome selective elimination analysis was performed for the two breeds of cattle using the fixation index (Fst) and nucleotide diversity (θπ ratio), and enrichment analysis was performed to explore their biological functions further. Both breeds exhibited relatively rich genetic diversity, with the Chinese Simmental cattle demonstrating higher genetic diversity than Xinjiang brown cattle. The IBS and G matrix results indicated that most individuals in the two populations were farther apart from each other. The PCA and neighbor-joining tree revealed no hybridization between the two breeds, but there was a certain degree of genetic differences among the individuals in the two breeds. Population structure analysis revealed that the optimal number of ancestors was three when K = 3. This resulted in clear genetic differentiation between the two populations, with only a few individuals having one ancestor and the majority having two or three common ancestors. A combined analysis of Fst and θπ was used to screen 112 candidate genes related to milk production traits in Xinjiang brown cattle and Chinese Simmental cattle. This study used genome-wide SNP markers to reveal the genetic diversity, population structure, and selection characteristics of two breeds. This study also screened candidate genes related to milk production traits, providing a theoretical basis for conserving genetic resources and improving genetic selection for milk production traits in Xinjiang brown cattle and Chinese Simmental cattle.

Uncovering the genetic basis of milk production traits in Mexican Holstein cattle based on individual markers and genomic windows

The objective of this study was to evaluate the proportion of genetic variance explained by single nucleotide polymorphism markers, individually or clustered in 1, 2, and 5 Mb windows, for milk yield, fat yield, protein yield, fat content, protein content, and somatic cell score in Mexican Holstein cattle. The analysis included data from 640,746 lactation records of 358,857 cows born between 1979 and 2019, distributed in 353 herds in 18 states of Mexico. The analysis included genotypic data on 7,713 cows and 577 sires, with information on 88,911 markers previously imputed and filtered by quality control. Genomic scans via the single-step genomic best linear unbiased prediction method were performed using BLUPF90 software. A total of 162 markers were significantly associated (p<0.01) with the phenotypic traits evaluated, and the SNP markers were distributed across chromosomes 1, 3, 5, 6, 10, 12, 14, 16, 18, 20, 22, and 29. When the size of the genomic windows was increased from 1 to 5 Mb, a greater proportion of genetic variance was explained by the SNPs within the window, and a greater number of windows explained more than 1% of the genetic variance. The most significant regions were associated with two or more phenotypic traits, such as one region on chromosome 14 that harbors the DGAT1, EXOSC4, PPP1R16A, and FOXH1 genes, which affect all the traits under study. In general, the utilization of genomic windows resulted in a greater proportion of genetic variance explained by milk production traits.

Genome-wide association studies of milk composition traits in indicine Badri cattle using ddRAD sequencing approach

Genome-wide association studies (GWAS) offer potential for discovering genomic regions that can be exploited to increase milk production. However, available GWAS and single nucleotide polymorphism (SNP) datasets are heavily skewed towards taurine breeds, which restricts their utility for genomic research in indicine cattle breeds. This study conducts a GWAS on the Badri breed of Indicine cattle to estimate variance components and identify significant variants associated with milk composition traits, utilizing double digest restriction-site associated DNA (ddRAD) sequencing data. A total of 65,483 high-confidence SNPs were identified and utilized to conduct GWAS on various milk composition traits, including fat percent (FP), protein percent (PP), casein percent (CP), lactose percent (LP), glucose percent (GP), galactose percent (GLP), total solids percent (TS), and solids-not-fat percent (SNF), each analysed separately. The heritability estimates for the studied milk composition traits were 0.386 for fat percent (FP), 0.427 for protein percent (PP), 0.469 for casein percent (CP), 0.567 for lactose percent (LP), 0.547 for glucose percent (GP), 0.590 for galactose percent (GLP), 0.437 for total solids percent (TS), and 0.476 for solids-not-fat percent (SNF). Several genomic regions and candidate genes, including SLC9A9, LPP, C2H2orf76, LGSN, HMGCS2, Bv1, SCYL2, PLAC8, SRGAP2, CR2, ZNF787, OTUB2, DSC2, SYNPO2, and CTNNA3 which may have a potential role in regulating milk production in indicine cattle were identified. The high confidence SNPs and candidate genes will be an important inclusion into commercial genotyping arrays for the early and best selection of breeding animals for desired milk composition and improved production.

Simultaneous Effects of Single-Nucleotide Polymorphisms on the Estimated Breeding Value of Milk, Fat, and Protein Yield of Holstein Friesian Cows in Hungary

This study aimed to find SNPs that have an effect on the estimated breeding values (EBVs) of milk (MY), fat (FY), and protein yield (PY) of Holstein Friesian cows in Hungary. Holstein Friesian cows (n = 2963) were genotyped on a Eurogenomics (EuroG_MDv4) chip. The EBVs for MY, FY, and PY were obtained from the Association of Hungarian Holstein Breeders (AHHB). The loci associated with the EBVs were identified via three approaches: the calculation of genetic distance of the SNPs (Fst_marker), linear regression, and haplotype association tests. Nine SNPs were significantly associated with MY, FY, and PY located on BTA 2, 5, 28, and X. Among the nine SNPs identified, BTB-00219372 on BTA 5 had a positive β coefficient for MY and a negative β coefficient for FY and PY. In addition, BovineHD3000027615 on BTA X had a positive β coefficient for both MY and PY, as well as a negative β coefficient for FY. The identified SNPs were located near several genes that remain unstudied in cattle, which are potential targets for closer scrutiny in relation to milk properties. The markers associated with two or three EBVs could be used in selection with high efficiency to accelerate genetic development and help AHHB experts achieve their breeding. Most marker effects point in the same direction on EBVs; however, we found that BTB-00219372 and BovineHD3000027615 could be used with caution to increase one EBV while decreasing the other EBV or EBVs.

Polymorphisms Within the IQGAP2 and CRTAC1 Genes of Gannan Yaks and Their Association with Milk Quality Characteristics

The IQ motif containing GTPase activating protein 2 (IQGAP2) gene functions as a tumor suppressor, reducing the malignant properties of breast cancer cells. The circulating cartilage acidic protein 1 (CRTAC1) gene, present in the whey protein fraction of dairy cows throughout lactation, is significantly correlated with fatty acids in milk. In this study, we investigated the correlation between single nucleotide polymorphisms (SNPs) in the IQGAP2 and CRTAC1 genes and milk quality traits in Gannan yaks, aiming to identify potential molecular marker loci for enhancing milk quality. Using the Illumina Yak cGPS 7K liquid chip, we genotyped 162 yaks and identified five SNPs in the IQGAP2 (g.232,769C>G, g.232,922G>C) and CRTAC1 (g.4,203T>C, g.5,348T>G, g.122,451T>C) genes. Genetic polymorphism analysis revealed that these five SNPs were moderately polymorphic and in Hardy-Weinberg equilibrium. An association analysis results showed that, at the g.232,769C>G locus of the IQGAP2 gene, the heterozygous CG genotype had significantly higher lactose content than the CC and GG homozygous genotypes (p < 0.05). Similarly, at the g.232,922G>C locus, the heterozygous GC and mutant CC genotypes significantly increased the contents of milk fat, lactose, and total solids (TS) (p < 0.05). In the CRTAC1 gene (g.4,203T>C, g.5,348T>G, g.122,451T>C), the mutant CC genotype significantly increased milk fat content, while the heterozygous TG genotype significantly increased lactose content (p < 0.05). In summary, mutations at the loci of g.232,769C>G, g.232,922G>C, g.4,203T>C, g.5,348T>G, and g.122,451T>C significantly elevated the lactose, milk fat, and TS content in Gannan yak milk, providing potential molecular marker candidates for improving Gannan yak milk quality.

Genome-wide selection reveals candidate genes associated with multiple teats in Hu sheep

Increasing the number of teats in sheep helps to improve the survival rate of sheep lambs after birth. In order to analyze the candidate genes related to the formation of multiple teats in Hu sheep, the present study was conducted to investigate the genetic pattern of multiple teats in Hu sheep. In this study, based on genome-wide data from 157 Hu sheep, Fst, xp-EHH, Pi and iHS signaling were performed, and the top 5% signal regions of each analyzed result were annotated based on the Oar_v4.0 for sheep. The results show that a total of 142 SNP loci were selected. We found that PTPRG, TMEM117 and LRP1B genes were closely associated with polypodium formation in Hu sheep, in addition, among the candidate genes related to polypodium we found genes such as TMEM117, SLC25A21 and NCKAP5 related to milk traits. The present study screened out candidate genes for the formation of multiple teats at the genomic level in Hu sheep.

Weighted single step GWAS reveals genomic regions associated with economic traits in Murrah buffaloes

The objective of the present study was to identify genomic regions influencing economic traits in Murrah buffaloes using weighted single step Genome Wide Association Analysis (WssGWAS). Data on 2000 animals, out of which 120 were genotyped using a double digest Restriction site Associated DNA (ddRAD) sequencing approach. The phenotypic data were collected from NDRI, India, on growth traits, viz., body weight at 6M (month), 12M, 18M and 24M, production traits like 305D (day) milk yield, lactation length (LL) and dry period (DP) and reproduction traits like age at first calving (AFC), calving interval (CI) and first service period (FSP). The biallelic genotypic data consisted of 49353 markers post-quality check. The heritability estimates were moderate to high, low to moderate, low for growth, production, reproduction traits, respectively. Important genomic regions explaining more than 0.5% of the total additive genetic variance explained by 30 adjacent SNPs were selected for further analysis of candidate genes. In this study, 105 genomic regions were associated with growth, 35 genomic regions with production and 42 window regions with reproduction traits. Different candidate genes were identified in these genomic regions, of which important are OSBPL8, NAP1L1 for growth, CNTNAP2 for production and ILDR2, TADA1 and POGK for reproduction traits.

The Genetic Characteristics of FT-MIRS-Predicted Milk Fatty Acids in Chinese Holstein Cows

Fourier Transform Mid-Infrared Spectroscopy (FT-MIRS) can be used for quantitative detection of milk components. Here, milk samples of 458 Chinese Holstein cows from 11 provinces in China were collected and we established a total of 22 quantitative prediction models in milk fatty acids by FT-MIRS. The coefficient of determination of the validation set ranged from 0.59 (C18:0) to 0.76 (C4:0). The models were adopted to predict the milk fatty acids from 2138 cows and a new high-throughput computing software HiBLUP was employed to construct a multi-trait model to estimate and analyze genetic parameters in dairy cows. Finally, genome-wide association analysis was performed and seven novel SNPs significantly associated with fatty acid content were selected, investigated, and verified with the FarmCPU method, which stands for "Fixed and random model Circulating Probability Unification". The findings of this study lay a foundation and offer technical support for the study of fatty acid trait breeding and the screening and grouping of characteristic dairy cows in China with rich, high-quality fatty acids. It is hoped that in the future, the method established in this study will be able to screen milk sources rich in high-quality fatty acids.

Study on Single Nucleotide Polymorphism of LAP3 Gene and Its Correlation with Dairy Quality Traits of Gannan Yak

This study explored the polymorphism of the leucine aminopeptidase (LAP3) gene and its relationship with milk quality characteristics in Gannan yak. A cohort of 162 Gannan yak was genotyped utilizing the Illumina Yak cGPS 7K BeadChip, and the identified single nucleotide polymorphisms (SNPs) were evaluated for their association with milk protein, casein, lactose, and fat concentrations. The results showed that four SNPs (g.4494G > A, g.5919A > G, g.8033G > C, and g.15,615A > G) in the LAP3 gene exhibited polymorphism with information content values of 0.267, 0.267, 0.293, and 0.114, respectively. All four SNPs were in Hardy-Weinberg equilibrium (p > 0.05). The g.4494G > A and g.5919A > G SNPs were significantly associated with protein content (p < 0.05), with homozygous genotypes showing significantly higher protein content than heterozygous genotypes (p < 0.05). The g.8033G > C SNP was significantly associated with casein content, protein content, non-fat solids, and acidity (p < 0.05), with the CC genotype having significantly higher casein, protein, and non-fat solids content than the GG and GC genotypes (p < 0.05). The g.15,615A > G SNP was significantly associated with average fat globule diameter (p < 0.05). In general, the mutations within the LAP3 gene demonstrated a positive impact on milk quality traits in Gannan yak, with mutated genotypes correlating with enhanced milk quality. These results indicate that the LAP3 gene could be a significant or candidate gene affecting milk quality traits in Gannan yak and offer potential genetic markers for molecular breeding programs in this species.

Genome-wide copy number variant analysis reveals candidate genes associated with milk production traits in water buffalo (Bubalus bubalis)

Buffaloes are vital contributors to the global dairy industry. Understanding the genetic basis of milk production traits in buffalo populations is essential for breeding programs and improving productivity. In this study, we conducted whole-genome resequencing on 387 buffalo genomes from 29 diverse Asian breeds, including 132 river buffaloes, 129 swamp buffaloes, and 126 crossbred buffaloes. We identified 36,548 copy number variants (CNV) spanning 133.29 Mb of the buffalo genome, resulting in 2,100 CNV regions (CNVR), with 1,993 shared CNVR being found within the studied buffalo types. Analyzing CNVR highlighted distinct genetic differentiation between river and swamp buffalo subspecies, verified by evolutionary tree and principal component analyses. Admixture analysis grouped buffaloes into river and swamp categories, with crossbred buffaloes displaying mixed ancestry. To identify candidate genes associated with milk production traits, we employed 3 approaches. First, we used Vst-based population differentiation, revealing 11 genes within CNVR that exhibited significant divergence between different buffalo breeds, including genes linked to milk production traits. Second, expression quantitative loci analysis revealed differentially expressed CNVR-derived genes (DECG) associated with milk production traits. Notably, known milk production-related genes were among these DECG, validating their relevance. Last, a GWAS identified 3 CNVR significantly linked to peak milk yield. Our study provides comprehensive genomic insights into buffalo populations and identifies candidate genes associated with milk production traits. These findings facilitate genetic breeding programs aimed at increasing milk yield and improving quality in this economically important livestock species.

Investigating the genomic background of calving-related traits in Canadian Jersey cattle

Traits related to calving have a significant impact on animal welfare and farm profitability in dairy production systems. Identifying genomic regions associated with calving traits could contribute to refining dairy cattle breeding programs and management practices in the dairy industry. Therefore, the primary objectives of this study were to estimate genetic parameters and perform genome-wide association studies (GWAS) and functional enrichment analyses for stillbirth, gestation length, calf size, and calving ease traits in North American Jersey cattle. A total of 40,503 animals with phenotypic records and 5,398 animals genotyped for 45,101 single nucleotide polymorphisms (SNPs) were included in the analyses. Genetic parameters were estimated based on animal models and Bayesian methods. The effects of SNPs were estimated using the Single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) method. The heritability (standard error) estimates ranged from 0.01 (0.01) for stillbirths (SB) in heifers to 0.11 (0.01) for gestation length (GL) in cows. The genetic correlations ranged from -0.58 (0.11) between calving ease (CE) and SB in heifers to 0.44 (0.14) between calving ease and calf size (CZ) in cows. CE showed the highest genetic correlation between heifers and cows, 0.8 (0.22) respectively. The candidate genes identified, including MTHFR, SERPINA5, IGFBP3, and ZRANB1, are involved in key biological processes and metabolic pathways related to the studied traits. Reducing environmental variation and identifying novel indicators of reproduction traits in the Jersey breed are needed given the low heritability estimates for most traits evaluated in this study. In conclusion, this study provides a characterization of the genetic background of calving-related traits in Jersey cattle. The estimates obtained can be used to improve or build selection indexes in Jersey cattle breeding programs in North America.

Genomic predictions and GWAS for heat tolerance in pigs based on reaction norm models with performance records and data from public weather stations considering alternative temperature thresholds

Genetic improvement of livestock productivity has resulted in greater production of metabolic heat and potentially greater susceptibility to heat stress. Various studies have demonstrated that there is genetic variability for heat tolerance and genetic selection for more heat tolerant individuals is possible. The rate of genetic progress tends to be greater when genomic information is incorporated into the analyses as more accurate breeding values can be obtained for young individuals. Therefore, this study aimed (1) to evaluate the predictive ability of genomic breeding values for heat tolerance based on routinely recorded traits, and (2) to investigate the genetic background of heat tolerance based on single-step genome-wide association studies for economically important traits related to body composition, growth and reproduction in Large White pigs. Pedigree information was available for 265,943 animals and genotypes for 8686 animals. The studied traits included ultrasound backfat thickness (BFT), ultrasound muscle depth (MDP), piglet weaning weight (WW), off-test weight (OTW), interval between farrowing (IBF), total number of piglets born (TNB), number of piglets born alive (NBA), number of piglets born dead (NBD), number of piglets weaned (WN) and weaning-to-estrus interval (IWE). The number of phenotypic records ranged from 6059 (WN) to 172,984 (TNB). Single-step genomic reaction norm predictions were used to calculate the genomic estimated breeding values for each individual. Predictions of breeding values for the validation population individuals were compared between datasets containing phenotypic records measured in the whole range of temperatures (WR) and datasets containing only phenotypic records measured when the weather station temperature was above 10°C (10C) or 15°C (15C), to evaluate the usefulness of these datasets that may better reflect the within-barn temperature. The use of homogeneous or heterogeneous residual variance was found to be trait-dependent, where homogeneous variance presented the best fit for MDP, BFT, OTW, TNB, NBA, WN and IBF, while the other traits (WW and IWE) had better fit with heterogeneous variance. The average prediction accuracy, dispersion and bias values considering all traits for WR were 0.36 ± 0.05, -0.07 ± 0.13 and 0.76 ± 0.10, respectively; for 10C were 0.39 ± 0.02, -0.05 ± 0.07 and 0.81 ± 0.05, respectively; and for 15C were 0.32 ± 0.05, -0.05 ± 0.11 and 0.84 ± 0.10, respectively. Based on the studied traits, using phenotypic records collected when the outside temperature (from public weather stations) was above 10°C provided better predictions for most of the traits. Forty-three and 62 candidate genomic regions were associated with the intercept (overall performance level) and slope term (specific biological mechanisms related to environmental sensitivity), respectively. Our results contribute to improve genomic predictions using existing datasets and better understand the genetic background of heat tolerance in pigs. Furthermore, the genomic regions and candidate genes identified will contribute to future genomic studies and breeding applications.

Genetic analysis of milk citrate predicted by milk mid-infrared spectra of Holstein cows in early lactation

Milk citrate is regarded as an early biomarker of negative energy balance in dairy cows during early lactation and serves as a suitable candidate phenotype for genomic selection due to its wide availability across a large number of cows through milk mid-infrared spectra prediction. However, its genetic background is not well known. Therefore, the objectives of this study were to (1) analyze the genetic parameters of milk citrate; (2) identify genomic regions associated with milk citrate; and (3) analyze the functional annotation of candidate genes and quantitative trait loci (QTL) related to milk citrate in Walloon Holstein cows. In total, 134,517 test-day milk-citrate phenotypes (mmol/L) collected within the first 50 d in milk on 52,198 Holstein cows were used. These milk-citrate phenotypes, predicted by milk mid-infrared spectra, were divided into 3 traits according to the first (citrate1), second (citrate2), and third to fifth parity (citrate3+). Genomic information for 566,170 SNPs was available for 4,479 animals. A multiple-trait repeatability model was used to estimate genetic parameters. A single-step GWAS was used to identify candidate genes for citrate and post-GWAS analysis was done to investigate the relationship and function of the identified candidate genes. The heritabilities estimated for citrate1, citrate2, and citrate3+ were 0.40, 0.37, and 0.35, respectively. The genetic correlations among the 3 traits ranged from 0.98 to 0.99. The genomic correlations among the 3 traits were also close to 1.00 across the genomic regions (1 Mb) in the whole genome, which means that citrate can be considered as a single trait in the first 5 parities. In total, 603 significant SNPs located on 3 genomic regions (chromosome 7, 68.569-68.575 Mb; chromosome 14, 0.15-1.90 Mb; and chromosome 20, 54.00-64.28 Mb), were identified to be associated with milk citrate. We identified 89 candidate genes including GPT, ANKH, PPP1R16A, and 32 QTL reported in the literature related to the identified significant SNPs. These identified QTL were mainly reported associated with milk fatty acids and metabolic diseases in dairy cows. This study suggests that milk citrate in Holstein cows is highly heritable and has the potential to be used as an early proxy for the negative energy balance of Holstein cows in a breeding objective.

Developmental programming of production and reproduction in dairy cows: IV. Association of maternal milk fat and protein percentage and milk fat to protein ratio with offspring's birth weight, survival, productive and reproductive performance and AMH concentration from birth to the first lactation period

Although the association of maternal milk production with developmental programming of offspring has been investigated, there is limited information available on the relationship of maternal milk components with productive and reproductive performance of the offspring. Therefore, the present study was conducted to analyze the association of maternal milk fat and protein percentage and milk fat to protein ratio with birth weight, survival, productive and reproductive performance and AMH concentration in the offspring. In study I, data of birth weight, milk yield and reproductive variables of offspring born to lactating dams (n = 14,582) and data associated with average maternal milk fat percentage (MFP), protein percentage (MPP) and fat to protein ratio (MFPR) during 305-day lactation were retrieved. Afterwards, offspring were classified in various categories of MFP, MPP and MFPR. In study II, blood samples (n = 339) were collected from offspring in various categories of MFP, MPP and MFPR for measurement of serum AMH. Maternal milk fat percentage was positively associated with birth weight and average percentage of milk fat (APMF) and protein (APMP) and milk fat to protein ratio (FPR) during the first lactation, but negatively associated with culling rate during nulliparity in the offspring (P < 0.05). Maternal milk protein percentage was positively associated with birth weight, APMF, APMP, FPR and culling rate, but negatively associated with milk yield and fertility in the offspring (P < 0.05). Maternal FPR was positively associated with APMF and FPR, but negatively associated with culling rate, APMP and fertility in the offspring (P < 0.05). However, concentration of AMH in the offspring was not associated with MFP, MPP and MFPR (P > 0.05). In conclusion, the present study revealed that maternal milk fat and protein percentage and their ratio were associated with birth weight, survival, production and reproduction of the offspring. Yet it was a preliminary research and further studies are required to elucidate the mechanisms underlying these associations.

Validation of Bos taurus SNPs for Milk Productivity of Sahiwal Breed (Bos indicus), Pakistan

The aim of the present study was the validation of the already reported Bos taurus SNPs in the Sahiwal breed. A total of nine SNPs of the casein gene were studied. Out of nine, seven Bos taurus SNPs of casein protein genes were found to be significantly associated with milk productivity traits. The genomic DNA was extracted from the mammary alveolar endothelial cells of a flock of 80 purebred Sahiwal lactating dams available at Khizrabad Farm near Sargodha. New allele-specific primers were designed from the NCBI annotated sequence database of Bos taurus to obtain 100 nt-long PCR products. Each dam was tested separately for all the SNPs investigated. Animals with genotype GG for the SNPs rs43703010, rs10500451, and 110323127, respectively, exhibited high milk yield. Similarly, animals with genotype AA for the SNPs rs11079521, rs43703016, and rs43703017 showed high milk yield consistently. For the SNP rs43703015, animals with genotype CC showed high milk productivity. These above-mentioned SNPs have previously been reported to significantly up-regulate casein protein contents in Bos taurus. Our results indicated SNPs that significantly affect the milk protein contents may also significantly increase per capita milk yield. These finding suggest that the above-mentioned reported SNPs can also be used as genetic markers of milk productivity in Sahiwal cattle.

Genome-wide mining of diversity and evolutionary signatures revealed selective hotspots in Indian Sahiwal cattle

The Sahiwal cattle breed is the best indigenous dairy cattle breed, and it plays a pivotal role in the Indian dairy industry. This is due to its exceptional milk-producing potential, adaptability to local tropical conditions, and its resilience to ticks and diseases. The study aimed to identify selective sweeps and estimate intrapopulation genetic diversity parameters in Sahiwal cattle using ddRAD sequencing-based genotyping data from 82 individuals. After applying filtering criteria, 78,193 high-quality SNPs remained for further analysis. The population exhibited an average minor allele frequency of 0.221 ± 0.119. Genetic diversity metrics, including observed (0.597 ± 0.196) and expected heterozygosity (0.433 ± 0.096), nucleotide diversity (0.327 ± 0.114), the proportion of polymorphic SNPs (0.726), and allelic richness (1.323 ± 0.134), indicated ample genomic diversity within the breed. Furthermore, an effective population size of 74 was observed in the most recent generation. The overall mean linkage disequilibrium (r2) for pairwise SNPs was 0.269 ± 0.057. Moreover, a greater proportion of short Runs of Homozygosity (ROH) segments were observed suggesting that there may be low levels of recent inbreeding in this population. The genomic inbreeding coefficients, computed using different inbreeding estimates (FHOM, FUNI, FROH, and FGROM), ranged from -0.0289 to 0.0725. Subsequently, we found 146 regions undergoing selective sweeps using five distinct statistical tests: Tajima's D, CLR, |iHS|, |iHH12|, and ROH. These regions, located in non-overlapping 500 kb windows, were mapped and revealed various protein-coding genes associated with enhanced immune systems and disease resistance (IFNL3, IRF8, BLK), as well as production traits (NRXN1, PLCE1, GHR). Notably, we identified interleukin 2 (IL2) on Chr17: 35217075-35223276 as a gene linked to tick resistance and uncovered a cluster of genes (HSPA8, UBASH3B, ADAMTS18, CRTAM) associated with heat stress. These findings indicate the evolutionary impact of natural and artificial selection on the environmental adaptation of the Sahiwal cattle population.

Association between Single Nucleotide Polymorphisms of PRKD1 and KCNQ3 Gene and Milk Quality Traits in Gannan Yak (Bos grunniens)

Protein kinase D1 (PRKD1) functions primarily in normal mammary cells, and the potassium voltage-gated channel subfamily Q member 3 (KCNQ3) gene plays an important role in controlling membrane potential and neuronal excitability, it has been found that this particular gene is linked to the percentage of milk fat in dairy cows. The purpose of this study was to investigate the relationship between nucleotide polymorphisms (SNPs) of PRKD1 and KCNQ3 genes and the milk quality of Gannan yak and to find molecular marker sites that may be used for milk quality breeding of Gannan yak. Three new SNPs were detected in the PRKD1 (g.283,619T>C, g.283,659C>A) and KCNQ3 gene (g.133,741T>C) of 172 Gannan lactating female yaks by Illumina yak cGPS 7K liquid-phase microarray technology. Milk composition was analyzed using a MilkoScanTM milk composition analyzer. We found that the mutations of these three loci significantly improved the lactose, milk fat, casein, protein, non-fat milk solid (SNF) content and acidity of Gannan yaks. The lactose content of the TC heterozygous genotype population at g.283,619T>C locus was significantly higher than that of the TT wild-type population (p < 0.05); the milk fat content of the CA heterozygous genotype population at g.283,659C>A locus was significantly higher than that of the CC wild-type and AA mutant populations (p < 0.05); the casein, protein and acidity of the CC mutant and TC heterozygous groups at the g.133,741T>C locus were significantly higher than those of the wild type (p < 0.05), and the SNF of the TC heterozygous group was significantly higher than that of the mutant group (p < 0.05). The results showed that PRKD1 and KCNQ3 genes could be used as candidate genes affecting the milk traits of Gannan yak.

Polymorphisms of CCSER1 Gene and Their Correlation with Milk Quality Traits in Gannan Yak (Bos grunniens)

Coiled-coil serine-rich protein 1 (CCSER 1) gene is a regulatory protein gene. This gene has been reported to be associated with various economic traits in large mammals in recent years. The aim of this study was to investigate the association between CCSER1 gene single nucleotide polymorphisms (SNPs) and Gannan yaks and to identify potential molecular marker loci for breeding milk quality in Gannan yaks. We genotyped 172 Gannan yaks using Illumina Yak cGPS 7K liquid microarrays and analyzed the correlation between the three SNPs loci of the CCSER1 gene and the milk qualities of Gannan yaks, including milk fat, protein and casein. It was found that mutations at the g.183,843A>G, g.222,717C>G and g.388,723G>T loci all affected the fat, protein, casein and lactose traits of Gannan yak milk to varying extents, and that the milk quality of individuals with mutant phenotypes was significantly improved. Among them, the milk fat content of AG heterozygous genotype population at g.183,843A>G locus was significantly higher than that of AA and GG genotype populations (p < 0.05); the casein and protein content of mutant GG and CG genotype populations at g.222,717C>G locus was significantly higher than that of wild-type CC genotype population (p < 0.05); and the g.388,723G>T locus of the casein and protein contents of the mutant TT genotype population were significantly higher (p < 0.05) than those of the wild-type GG genotype population. These results provide potential molecular marker sites for Gannan yak breeding.

Exploring genome-wide differentiation and signatures of selection in Italian and North American Holstein populations

Among Italian dairy cattle, the Holstein is the most reared breed for the production of Parmigiano Reggiano protected designation of origin cheese, which represents one of the most renowned products in the entire Italian dairy industry. In this work, we used a medium-density genome-wide data set consisting of 79,464 imputed SNPs to study the genetic structure of Italian Holstein breed, including the population reared in the area of Parmigiano Reggiano cheese production, and assessing its distinctiveness from the North American population. Multidimensional scaling and ADMIXTURE approaches were used to explore the genetic structure among populations. We also investigated putative genomic regions under selection among these 3 populations by combining 4 different statistical methods based either on allele frequencies (single marker and window-based) or extended haplotype homozygosity (EHH; standardized log-ratio of integrated EHH and cross-population EHH). The genetic structure results allowed us to clearly distinguish the 3 Holstein populations; however, the most remarkable difference was observed between Italian and North American stock. Selection signature analyses identified several significant SNPs falling within or closer to genes with known roles in several traits such as milk quality, resistance to disease, and fertility. In particular, a total of 22 genes related to milk production have been identified using the 2 allele frequency approaches. Among these, a convergent signal has been found in the VPS8 gene which resulted to be involved in milk traits, whereas other genes (CYP7B1, KSR2, C4A, LIPE, DCDC1, GPR20, and ST3GAL1) resulted to be associated with quantitative trait loci related to milk yield and composition in terms of fat and protein percentage. In contrast, a total of 7 genomic regions were identified combining the results of standardized log-ratio of integrated EHH and cross-population EHH. In these regions candidate genes for milk traits were also identified. Moreover, this was also confirmed by the enrichment analyses in which we found that the majority of the significantly enriched quantitative trait loci were linked to milk traits, whereas the gene ontology and pathway enrichment analysis pointed to molecular functions and biological processes involved in AA transmembrane transport and methane metabolism pathway. This study provides information on the genetic structure of the examined populations, showing that they are distinguishable from each other. Furthermore, the selection signature analyses can be considered as a starting point for future studies in the identification of causal mutations and consequent implementation of more practical application.

Haplotype-based association study of production and reproduction traits in multigenerational Vrindavani population

Haplotype-based association analysis promises to reveal important information regarding the effect of genetic variants on economic traits of interest. The present study aimed to evaluate the haplotype structure of Vrindavani cattle and explore the association of haplotypes with (re)production traits of economic interest. Genotyping array data of medium density (Bovine50KSNP BeadChip) on 96 randomly selected Vrindavani cows was used in the present study. Genotypes were called in GenomeStudio program while quality control was undertaken in PLINK using standard thresholds. The phenotypic traits used in the present study included age at first calving, dry days, lactation length, peak yield, total lactation milk yield, inter-calving period and service period. The haplotype structure of Vrindavani population was assessed, using a sliding window of 20 SNP with a shift of 5 SNPs at a time, in terms of the size of haplotype blocks regarding their length (in Kb) and frequency in chromosome-wise fashion. Haplotype blocks were assessed for possible association with important production and reproduction traits across three lactation cycles in Vrindavani cattle population. The first ten principal components were included in the model for haplotype-based association analysis to correct for stratification effects of assessed individuals. Multiple haplotypes were found to be associated with age at first calving, total lactation milk yield, peak yield, dry days, inter-calving period and service period. Various candidate genes were found to overlap haplotypes that were significantly associated with age at first calving (CDH18, MARCHF11, MYO10, FBXL7), total lactation milk yield (TGF, PDE1A, and COL8A1), peak yield (PPARGC1A, RCAN1, KCNE1, SMIM34 and MRPS6), dry days (CPNE4, ACAD11 and MRAS), inter-calving period (ABCG5, ABCG8 and COX7A2L) and service period (FOXL2 and PIK3CB). The putative candidate genes overlapping the significantly associated haplotypes revealed important pathways affecting the production and reproduction performance of animals. The identified genes and pathways may serve as good candidate markers to select animals for improved production and reproduction performance in future generations.

Genome-wide association study and functional annotation analyses for nitrogen efficiency index and its composition traits in dairy cattle

The aims of this study were (1) to identify genomic regions associated with a N efficiency index (NEI) and its composition traits and (2) to analyze the functional annotation of identified genomic regions. The NEI included N intake (NINT1), milk true protein N (MTPN1), milk urea N yield (MUNY1) in primiparous cattle, and N intake (NINT2+), milk true protein N (MTPN2+), and milk urea N yield (MUNY2+) in multiparous cattle (2 to 5 parities). The edited data included 1,043,171 records on 342,847 cows distributed in 1,931 herds. The pedigree consisted of 505,125 animals (17,797 males). Data of 565,049 SNPs were available for 6,998 animals included in the pedigree (5,251 females and 1,747 males). The SNP effects were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of about 240 kb) was calculated. The top 3 genomic regions explaining the largest rate of the total additive genetic variance of the NEI and its composition traits were selected for candidate gene identification and quantitative trait loci (QTL) annotation. The selected genomic regions explained from 0.17% (MTPN2+) to 0.58% (NEI) of the total additive genetic variance. The largest explanatory genomic regions of NEI, NINT1, NINT2+, MTPN1, MTPN2+, MUNY1, and MUNY2+ were Bos taurus autosome 14 (1.52-2.09 Mb), 26 (9.24-9.66 Mb), 16 (75.41-75.51 Mb), 6 (8.73-88.92 Mb), 6 (8.73-88.92 Mb), 11 (103.26-103.41 Mb), 11 (103.26-103.41 Mb). Based on the literature, gene ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction, 16 key candidate genes were identified for NEI and its composition traits, which are mainly expressed in the milk cell, mammary, and liver tissues. The number of enriched QTL related to NEI, NINT1, NINT2+, MTPN1, and MTPN2+ were 41, 6, 4, 11, 36, 32, and 32, respectively, and most of them were related to the milk, health, and production classes. In conclusion, this study identified genomic regions associated with NEI and its composition traits, and identified key candidate genes describing the genetic mechanisms of N use efficiency-related traits. Furthermore, the NEI reflects not only its composition traits but also the interactions among them.

Non-Synonymous Variants in Fat QTL Genes among High- and Low-Milk-Yielding Indigenous Breeds

The effect of breed on milk components-fat, protein, lactose, and water-has been observed to be significant. As fat is one of the major price-determining factors for milk, exploring the variations in fat QTLs across breeds would shed light on the variable fat content in their milk. Here, on whole-genome sequencing, 25 differentially expressed hub or bottleneck fat QTLs were explored for variations across indigenous breeds. Out of these, 20 genes were identified as having nonsynonymous substitutions. A fixed SNP pattern in high-milk-yielding breeds in comparison to low-milk-yielding breeds was identified in the genes GHR, TLR4, LPIN1, CACNA1C, ZBTB16, ITGA1, ANK1, and NTG5E and, vice versa, in the genes MFGE8, FGF2, TLR4, LPIN1, NUP98, PTK2, ZTB16, DDIT3, and NT5E. The identified SNPs were ratified by pyrosequencing to prove that key differences exist in fat QTLs between the high- and low-milk-yielding breeds.

Longitudinal genome-wide association studies of milk production traits in Holstein cattle using whole-genome sequence data imputed from medium-density chip data

Longitudinal traits, such as milk production traits in dairy cattle, are featured by having phenotypic values at multiple time points, which change dynamically over time. In this study, we first imputed SNP chip (50-100K) data to whole-genome sequence (WGS) data in a Chinese Holstein population consisting of 6,470 cows. The imputation accuracies were 0.88 to 0.97 on average after quality control. We then performed longitudinal GWAS in this population based on a random regression test-day model using the imputed WGS data. The longitudinal GWAS revealed 16, 39, and 75 quantitative trait locus regions associated with milk yield, fat percentage, and protein percentage, respectively. We estimated the 95% confidence intervals (CI) for these quantitative trait locus regions using the logP drop method and identified 581 genes involved in these CI. Further, we focused on the CI that covered or overlapped with only 1 gene or the CI that contained an extremely significant top SNP. Twenty-eight candidate genes were identified in these CI. Most of them have been reported in the literature to be associated with milk production traits, such as DGAT1, HSF1, MGST1, GHR, ABCG2, ADCK5, and CSN1S1. Among the unreported novel genes, some also showed good potential as candidate genes, such as CCSER1, CUX2, SNTB1, RGS7, OSR2, and STK3, and are worth being further investigated. Our study provided not only new insights into the candidate genes for milk production traits, but also a general framework for longitudinal GWAS based on random regression test-day model using WGS data.

Genotype-environment interaction for milk production of Gyr cattle in Brazil and Colombia

BACKGROUND

Genotype by environment interactions (G × E) can play an important role in cattle populations and should be included in breeding programs in order to select the best animals for different environments.

OBJECTIVE

The aim of this study was to investigate the G × E for milk production of Gyr cattle in Brazil and Colombia by applying a reaction norm model used genomics information, and to identify genomic regions associated with milk production in the two countries.

METHODS

The Brazilian and Colombian database included 464 animals (273 cows and 33 sires from Brazil and 158 cows from Colombia) and 27,505 SNPs. A two-trait animal model was used for milk yield adjusted to 305 days in Brazil and Colombia as a function of country of origin, which included genomic information obtained with a single-step genomic reaction norm model. The GIBBS3F90 and POSTGSf90 programs were used.

RESULTS

The results obtained indicate G × E based on the reranking of bulls between Brazil and Colombia, demonstrating environmental differences between the two countries. The findings highlight the importance of considering the environment when choosing breeding animals in order to ensure the adequate performance of their progeny. Within this context, the reranking of bulls and the different SNPs associated with milk production in the two countries suggest that G × E is an important effect that should be included in the genetic evaluation of Dairy Gyr cattle in Brazil and Colombia.

CONCLUSION

The Gyr breeding program can be optimized by choosing a selection environment that will allow maximum genetic progress in milk production in different environments within and between countries.

Whole-genome resequencing reveals genetic diversity and selection characteristics of dairy goat

The dairy goat is one of the earliest dairy livestock species, which plays an important role in the economic development, especially for developing countries. With the development of agricultural civilization, dairy goats have been widely distributed across the world. However, few studies have been conducted on the specific characteristics of dairy goat. In this study, we collected the whole-genome data of 89 goat individuals by sequencing 48 goats and employing 41 publicly available goats, including five dairy goat breeds (Saanen, Nubian, Alpine, Toggenburg, and Guanzhong dairy goat; n = 24, 15, 11, 6, 6), and three goat breeds (Guishan goat, Longlin goat, Yunshang Black goat; n = 6, 15, 6). Through compared the genomes of dairy goat and non-dairy goat to analyze genetic diversity and selection characteristics of dairy goat. The results show that the eight goats could be divided into three subgroups of European, African, and Chinese indigenous goat populations, and we also found that Australian Nubian, Toggenburg, and Australian Alpine had the highest linkage disequilibrium, the lowest level of nucleotide diversity, and a higher inbreeding coefficient, indicating that they were strongly artificially selected. In addition, we identified several candidate genes related to the specificity of dairy goat, particularly genes associated with milk production traits (GHR, DGAT2, ELF5, GLYCAM1, ACSBG2, ACSS2), reproduction traits (TSHR, TSHB, PTGS2, ESR2), immunity traits (JAK1, POU2F2, LRRC66). Our results provide not only insights into the evolutionary history and breed characteristics of dairy goat, but also valuable information for the implementation and improvement of dairy goat cross breeding program.

Genome-wide association study for selected cheese-making properties in Dual-Purpose Belgian Blue cows

This study aimed to estimate genetic parameters and identify genomic region(s) associated with selected cheese-making properties (CMP) in Dual-Purpose Belgian Blue (DPBB) cows. Edited data were 46,301 test-day records of milk yield, fat percentage, protein percentage, casein percentage, milk calcium content (CC), coagulation time (CT), curd firmness after 30 min from rennet addition (a30), and milk titratable acidity (MTA) collected from 2014 to 2020 on 4,077 first-parity (26,027 test-day records), and 3,258 second-parity DPBB cows (20,274 test-day records) distributed in 124 herds in the Walloon Region of Belgium. Data of 28,266 SNP, located on 29 Bos taurus autosomes (BTA) of 1,699 animals were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 25 consecutive SNPs (with an average size of ∼2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Heritability estimates for the included CMP ranged from 0.19 (CC) to 0.50 (MTA), and 0.24 (CC) to 0.41 (MTA) in the first and second parity, respectively. The genetic correlation estimated between CT and a30 varied from -0.61 to -0.41 and from -0.55 to -0.38 in the first and second lactations, respectively. Negative genetic correlations were found between CT and milk yield and composition, while those estimated between curd firmness and milk composition were positive. Genome-wide association analyses results identified 4 genomic regions (BTA1, BTA3, BTA7, and BTA11) associated with the considered CMP. The identified genomic regions showed contrasting results between parities and among the different stages of each parity. It suggests that different sets of candidate genes underlie the phenotypic expression of the considered CMP between parities and lactation stages of each parity. The findings of this study can be used for future implementation and use of genomic evaluation to improve the cheese-making traits in DPBB cows.

Genome-wide association analysis of milk production, somatic cell score, and body conformation traits in Holstein cows

Milk production and body conformation traits are critical economic traits for dairy cows. To understand the basic genetic structure for those traits, a genome wide association study was performed on milk yield, milk fat yield, milk fat percentage, milk protein yield, milk protein percentage, somatic cell score, body form composite index, daily capacity composite index, feed, and leg conformation traits, based on the Illumina Bovine HD100k BeadChip. A total of 57, 12 and 26 SNPs were found to be related to the milk production, somatic cell score and body conformation traits in the Holstein cattle. Genes with pleiotropic effect were also found in this study. Seven significant SNPs were associated with multi-traits and were located on the PLEC, PLEKHA5, TONSL, PTGER4, and LCORL genes. In addition, some important candidate genes, like GPAT3, CEBPB, AGO2, SLC37A1, and FNDC3B, were found to participate in fat metabolism or mammary gland development. These results can be used as candidate genes for milk production, somatic cell score, and body conformation traits of Holstein cows, and are helpful for further gene function analysis to improve milk production and quality.

Genome-Wide Association Study for Udder Conformation Traits in Chinese Holstein Cattle

Udder conformation traits are one of the most economic traits in dairy cows, greatly affecting animal health, milk production, and producer profitability in the dairy industry. Genetic analysis of udder structure and scores have been developed in Holstein cattle. In our research, we conducted a genome-wide association study for five udder traits, including anterior udder attachment (AUA), central suspensory ligament (CSL), posterior udder attachment height (PUAH), posterior udder attachment width (PUAW), and udder depth (UD), in which the fixed and random model circulating probability unification (FarmCPU) model was applied for the association analysis. The heritability and the standard errors of these five udder traits ranged from 0.04 ± 0.00 to 0.49 ± 0.03. Phenotype data were measured from 1000 Holstein cows, and the GeneSeek Genomic Profiler (GGP) Bovine 100 K SNP chip was used to analyze genotypic data in Holstein cattle. For GWAS analysis, 984 individual cows and 84,407 single-nucleotide polymorphisms (SNPs) remained after quality control; a total of 18 SNPs were found at the GW significant threshold (p < 5.90 × 10−7). Many candidate genes were identified within 200kb upstream or downstream of the significant SNPs, which include MGST1, MGST2, MTUS1, PRKN, STXBP6, GRID2, E2F8, CDH11, FOXP1, SLF1, TMEM117, SBF2, GC, ADGRB3, and GCLC. Pathway analysis revealed that 58 Gene Ontology (GO) terms and 18 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched with adjusted p values, and these GO terms and the KEGG pathway analysis were associated with biological information, metabolism, hormonal growth, and development processes. These results could give valuable biological information for the genetic architecture of udder conformation traits in dairy Holstein cattle.

Incorporating kernelized multi-omics data improves the accuracy of genomic prediction

Background

Genomic selection (GS) has revolutionized animal and plant breeding after the first implementation via early selection before measuring phenotypes. Besides genome, transcriptome and metabolome information are increasingly considered new sources for GS. Difficulties in building the model with multi-omics data for GS and the limit of specimen availability have both delayed the progress of investigating multi-omics.

Results

We utilized the Cosine kernel to map genomic and transcriptomic data as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}\times {n}$$\end{document}n×n symmetric matrix (G matrix and T matrix), combined with the best linear unbiased prediction (BLUP) for GS. Here, we defined five kernel-based prediction models: genomic BLUP (GBLUP), transcriptome-BLUP (TBLUP), multi-omics BLUP (MBLUP, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\boldsymbol M=\mathrm{ratio}\times\boldsymbol G+(1-\mathrm{ratio})\times\boldsymbol T$$\end{document}M=ratio×G+(1-ratio)×T), multi-omics single-step BLUP (mssBLUP), and weighted multi-omics single-step BLUP (wmssBLUP) to integrate transcribed individuals and genotyped resource population. The predictive accuracy evaluations in four traits of the Chinese Simmental beef cattle population showed that (1) MBLUP was far preferred to GBLUP (ratio = 1.0), (2) the prediction accuracy of wmssBLUP and mssBLUP had 4.18% and 3.37% average improvement over GBLUP, (3) We also found the accuracy of wmssBLUP increased with the growing proportion of transcribed cattle in the whole resource population.

Conclusions

We concluded that the inclusion of transcriptome data in GS had the potential to improve accuracy. Moreover, wmssBLUP is accepted to be a promising alternative for the present situation in which plenty of individuals are genotyped when fewer are transcribed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40104-022-00756-6.

Genome-wide association and genotype by environment interactions for growth traits in U.S. Red Angus cattle

Background

Genotypic information produced from single nucleotide polymorphism (SNP) arrays has routinely been used to identify genomic regions associated with complex traits in beef and dairy cattle. Herein, we assembled a dataset consisting of 15,815 Red Angus beef cattle distributed across the continental U.S. and a union set of 836,118 imputed SNPs to conduct genome-wide association analyses (GWAA) for growth traits using univariate linear mixed models (LMM); including birth weight, weaning weight, and yearling weight. Genomic relationship matrix heritability estimates were produced for all growth traits, and genotype-by-environment (GxE) interactions were investigated.

Results

Moderate to high heritabilities with small standard errors were estimated for birth weight (0.51 ± 0.01), weaning weight (0.25 ± 0.01), and yearling weight (0.42 ± 0.01). GWAA revealed 12 pleiotropic QTL (BTA6, BTA14, BTA20) influencing Red Angus birth weight, weaning weight, and yearling weight which met a nominal significance threshold (P ≤ 1e-05) for polygenic traits using 836K imputed SNPs. Moreover, positional candidate genes associated with Red Angus growth traits in this study (i.e., LCORL, LOC782905, NCAPG, HERC6, FAM184B, SLIT2, MMRN1, KCNIP4, CCSER1, GRID2, ARRDC3, PLAG1, IMPAD1, NSMAF, PENK, LOC112449660, MOS, SH3PXD2B, STC2, CPEB4) were also previously associated with feed efficiency, growth, and carcass traits in beef cattle. Collectively, 14 significant GxE interactions were also detected, but were less consistent among the investigated traits at a nominal significance threshold (P ≤ 1e-05); with one pleiotropic GxE interaction detected on BTA28 (24 Mb) for Red Angus weaning weight and yearling weight.

Conclusions

Sixteen well-supported QTL regions detected from the GWAA and GxE GWAA for growth traits (birth weight, weaning weight, yearling weight) in U.S. Red Angus cattle were found to be pleiotropic. Twelve of these pleiotropic QTL were also identified in previous studies focusing on feed efficiency and growth traits in multiple beef breeds and/or their composites. In agreement with other beef cattle GxE studies our results implicate the role of vasodilation, metabolism, and the nervous system in the genetic sensitivity to environmental stress.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08667-6.

Genome-wide association for milk urea concentration in Dual-Purpose Belgian Blue cows

The objectives of this study were to estimate genetic parameters and identify genomic regions associated with milk urea concentration (MU) in Dual-Purpose Belgian Blue (DPBB) cows. The data were 29,693 test-day records of milk yield (MY), fat yield (FY), protein yield (PY), fat percentage (FP), protein percentage (PP) and MU collected between 2014 and 2020 on 2498 first parity cows (16,935 test-day records) and 1939 second-parity cows (12,758 test-day records) from 49 herds in the Walloon Region of Belgium. Data of 28,266 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes (BTA), on 1699 animals (639 males and 1060 females) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method using a single chain of 100,000 iterations after a burn-in period of 20,000. SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by windows of 25 consecutive SNPs (with an average size of ~2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. The mean (SD) of MU was 22.89 (10.07) and 22.35 (10.07) mg/dl for first and second parity, respectively. The mean (SD) heritability estimates for daily MU were 0.18 (0.01) and 0.22 (0.02), for first and second parity, respectively. The mean (SD) genetic correlations between daily MU and MY, FY, PY, FP and PP were -0.05 (0.09), -0.07 (0.11), -0.03 (0.13), -0.05 (0.08) and -0.03 (0.11) for first parity, respectively. The corresponding values estimated for second parity were 0.02 (0.10), -0.02 (0.09), 0.02 (0.08), -0.08 (0.06) and -0.05 (0.05). The genome-wide association analyses identified three genomic regions (BTA2, BTA3 and BTA13) associated with MU. The identified regions showed contrasting results between parities and among different stages within each parity. This suggests that different groups of candidate genes underlie the phenotypic expression of MU between parities and among different lactation stages within a parity. The results of this study can be used for future implementation and use of genomic evaluation to reduce MU in DPBB cows.

Genetic architecture of a composite beef cattle population

Composite breeds are widely used in the beef industry. Composites allow producers to combine desirable traits from the progenitor breeds and simplify herd management, without repeated crossbreeding and maintenance of purebreds. In this study, genomic information was used to evaluate the genetic composition and characteristics of a three-breed beef cattle composite. This composite population referred to as Composite Gene Combination (CGC) consisted of 50% Red Angus, 25% Charolais, 25% Tarentaise. A total of 248 animals were used in this study CGC (n=79), Red Angus (n=61), Charolais (n=79) and Tarentaise (n=29). All animals were genotyped with 777k HD panel. Principal component and ADMIXTURE analyses were carried out to evaluate the genetic structure of CGC animals. The ADMIXTURE revealed the proportion of Tarentaise increased to approximately 57% while Charolais decreased to approximately 5%, and Red Angus decreased to 38% across generations. To evaluate these changes in the genomic composition across different breeds and in CGC across generations runs of homozygosity (ROH) were conducted. This analysis showed Red Angus to have the highest total length of ROH segments per animal with a mean of 349.92 Mb and lowest in CGC with a mean of 141.10 Mb. Furthermore, it showed the formation of new haplotypes in CGC around the sixth generation. Selection signatures were evaluated through Fst and HapFlk analyses. Several selection sweeps in CGC were identified especially in chromosomes 5 and 14 which have previously been reported to be associated with coat color and growth traits. The study supports our previous findings that progenitor combinations are not stable over generations and that either direct or natural selection plays a role in modifying the progenitor proportions. Furthermore, the results showed that Tarentaise contributed useful attributes to the composite in a cool semi-arid environment and suggests a re-exploration of this breed's role may be warranted.

Weighted Single-Step Genome-Wide Association Study Uncovers Known and Novel Candidate Genomic Regions for Milk Production Traits and Somatic Cell Score in Valle del Belice Dairy Sheep

Simple Summary

Milk production is the most economically crucial dairy sheep trait and constitutes the major genetic enhancement purpose via selective breeding. Also, mastitis is one of the most frequently encountered diseases, having a significant impact on animal welfare, milk yield, and quality. The aim of this study was to identify genomic region(s) associated with the milk production traits and somatic cell score (SCS) in Valle del Belice sheep using single-step genome-wide association (ssGWA) and genotyping data from medium density SNP panels. We identified several genomic regions (OAR1, OAR2, OAR3, OAR4, OAR6, OAR9, and OAR25) and candidate genes implicated in milk production traits and SCS. Our findings offer new insights into the genetic basis of milk production traits and SCS in dairy sheep.

Abstract

The objective of this study was to uncover genomic regions explaining a substantial proportion of the genetic variance in milk production traits and somatic cell score in a Valle del Belice dairy sheep. Weighted single-step genome-wide association studies (WssGWAS) were conducted for milk yield (MY), fat yield (FY), fat percentage (FAT%), protein yield (PY), protein percentage (PROT%), and somatic cell score (SCS). In addition, our aim was also to identify candidate genes within genomic regions that explained the highest proportions of genetic variance. Overall, the full pedigree consists of 5534 animals, of which 1813 ewes had milk data (15,008 records), and 481 ewes were genotyped with a 50 K single nucleotide polymorphism (SNP) array. The effects of markers and the genomic estimated breeding values (GEBV) of the animals were obtained by five iterations of WssGBLUP. We considered the top 10 genomic regions in terms of their explained genomic variants as candidate window regions for each trait. The results showed that top ranked genomic windows (1 Mb windows) explained 3.49, 4.04, 5.37, 4.09, 3.80, and 5.24% of the genetic variances for MY, FY, FAT%, PY, PROT%, and total SCS, respectively. Among the candidate genes found, some known associations were confirmed, while several novel candidate genes were also revealed, including PPARGC1A, LYPLA1, LEP, and MYH9 for MY; CACNA1C, PTPN1, ROBO2, CHRM3, and ERCC6 for FY and FAT%; PCSK5 and ANGPT1 for PY and PROT%; and IL26, IFNG, PEX26, NEGR1, LAP3, and MED28 for SCS. These findings increase our understanding of the genetic architecture of six examined traits and provide guidance for subsequent genetic improvement through genome selection.

Identification of Candidate Genes Regulating Carcass Depth and Hind Leg Circumference in Simmental Beef Cattle Using Illumina Bovine Beadchip and Next-Generation Sequencing Analyses

Genome-wide association studies are a robust means of identifying candidate genes that regulate economically important traits in farm animals. The aim of this study is to identify single-nucleotide polymorphisms (SNPs) and candidate genes potentially related to carcass depth and hind leg circumference in Simmental beef cattle. We performed Illumina Bovine HD Beadchip (~670 k SNPs) and next-generation sequencing (~12 million imputed SNPs) analyses of data from 1252 beef cattle, to which we applied a linear mixed model. Using a statistical threshold (p = 0.05/number of SNPs identified) and adopting a false discovery rate (FDR), we identified many putative SNPs on different bovine chromosomes. We identified 12 candidate genes potentially annotated with the markers identified, including CDKAL1 and E2F3, related to myogenesis and skeletal muscle development. The identification of such genes in Simmental beef cattle will help breeders to understand and improve related traits, such as meat yield.

Genome Wide Scan to Identify Potential Genomic Regions Associated With Milk Protein and Minerals in Vrindavani Cattle

In this study, genome-wide association study (GWAS) was conducted for identifying significantly associated genomic regions/SNPs with milk protein and minerals in the 96 taurine-indicine crossbred (Vrindavani) cows using 50K SNP Chip. After quality control, a total of 41,427 SNPs were retained and were further analyzed using a single-SNP additive linear model. Lactation stage, parity, test day milk yield and proportion of exotic inheritance were included as fixed effects in GWAS model. Across all traits, 13 genome-wide significant (p < 1.20 x 10⁻⁰⁶) and 49 suggestive significant (p < 2.41 x 10⁻⁰⁵) SNPs were identified which were located on 18 different autosomes. The strongest association for protein percentage, calcium (Ca), phosphorus (P), copper (Cu), zinc (Zn), and iron (Fe) were found on BTA 18, 7, 2, 3, 14, and 2, respectively. No significant SNP was detected for manganese (Mn). Several significant SNPs identified were within or close proximity to CDH13, BHLHE40, EDIL3, HAPLN1, INHBB, USP24, ZFAT, and IKZF2 gene, respectively. Enrichment analysis of the identified candidate genes elucidated biological processes, cellular components, and molecular functions involved in metal ion binding, ion transportation, transmembrane protein, and signaling pathways. This study provided a groundwork to characterize the molecular mechanism for the phenotypic variation in milk protein percentage and minerals in crossbred cattle. Further work is required on a larger sample size with fine mapping of identified QTL to validate potential candidate regions.

Copy number variants in genomes of local sheep breeds from Russia

Copy number variants (CNVs) are genomic structural variations that contribute to many adaptive and economically important traits in livestock. In this study, we detected CNVs in 354 animals from 16 Russian indigenous sheep breeds and analysed their possible functional roles. Our analysis of the entire sample set resulted in 4527 CNVs forming 1450 CNV regions (CNVRs). When constructing CNVRs for individual breeds, a total of 2715 regions ranging from 88 in Groznensk to 337 in Osetin breeds were identified. To make interbreed CNVR frequency comparison possible, we also identified core CNVRs using CNVs with overlapping chromosomal locations found in different breeds. This resulted in 137 interbreed CNVRs with frequency >15% in at least one breed. Functional enrichment analysis of genes affected by CNVRs in individual breeds revealed 12 breeds with significant enrichments in olfactory perception, PRAME family proteins, and immune response. Function of genes affected by interbreed and breed-specific CNVRs revealed candidates related to domestication, adaptation to high altitudes and cold climates, reproduction, parasite resistance, milk and meat qualities, wool traits, fat storage, and fat metabolism. Our work is the first attempt to uncover and characterise the CNV makeup of Russian indigenous sheep breeds. Further experimental and functional validation of CNVRs would help in developing new and improving existing sheep breeds.

Genomewide Association Analyses of Lactation Persistency and Milk Production Traits in Holstein Cattle Based on Imputed Whole-Genome Sequence Data

Lactation persistency and milk production are among the most economically important traits in the dairy industry. In this study, we explored the association of over 6.1 million imputed whole-genome sequence variants with lactation persistency (LP), milk yield (MILK), fat yield (FAT), fat percentage (FAT%), protein yield (PROT), and protein percentage (PROT%) in North American Holstein cattle. We identified 49, 3991, 2607, 4459, 805, and 5519 SNPs significantly associated with LP, MILK, FAT, FAT%, PROT, and PROT%, respectively. Various known associations were confirmed while several novel candidate genes were also revealed, including ARHGAP35, NPAS1, TMEM160, ZC3H4, SAE1, ZMIZ1, PPIF, LDB2, ABI3, SERPINB6, and SERPINB9 for LP; NIM1K, ZNF131, GABRG1, GABRA2, DCHS1, and SPIDR for MILK; NR6A1, OLFML2A, EXT2, POLD1, GOT1, and ETV6 for FAT; DPP6, LRRC26, and the KCN gene family for FAT%; CDC14A, RTCA, HSTN, and ODAM for PROT; and HERC3, HERC5, LALBA, CCL28, and NEURL1 for PROT%. Most of these genes are involved in relevant gene ontology (GO) terms such as fatty acid homeostasis, transporter regulator activity, response to progesterone and estradiol, response to steroid hormones, and lactation. The significant genomic regions found contribute to a better understanding of the molecular mechanisms related to LP and milk production in North American Holstein cattle.

Integrated Small RNA Sequencing, Transcriptome and GWAS Data Reveal microRNA Regulation in Response to Milk Protein Traits in Chinese Holstein Cattle

Milk protein is one of the most important economic traits in the dairy industry. Yet, the regulatory network of miRNAs for the synthesis of milk protein in mammary is poorly understood. Samples from 12 Chinese Holstein cows with three high ( ≥ 3.5%) and three low ( ≤ 3.0%) phenotypic values for milk protein percentage in lactation and non-lactation were examined through deep small RNA sequencing. We characterized 388 known and 212 novel miRNAs in the mammary gland. Differentially expressed analysis detected 28 miRNAs in lactation and 52 miRNAs in the non-lactating period with a highly significant correlation with milk protein concentration. Target prediction and correlation analysis identified some key miRNAs and their targets potentially involved in the synthesis of milk protein. We analyzed for enrichments of GWAS signals in miRNAs and their correlated targets. Our results demonstrated that genomic regions harboring DE miRNA genes in lactation were significantly enriched with GWAS signals for milk protein percentage traits and that enrichments within DE miRNA targets were significantly higher than in random gene sets for the majority of milk production traits. This integrated study on the transcriptome and posttranscriptional regulatory profiles between significantly differential phenotypes of milk protein concentration provides new insights into the mechanism of milk protein synthesis, which should reveal the regulatory mechanisms of milk secretion.

Genome-wide association study on milk production and somatic cell score for Thai dairy cattle using weighted single-step approach with random regression test-day model

Genome-wide association studies are a powerful tool to identify genomic regions and variants associated with phenotypes. However, only limited mutual confirmation from different studies is available. The objectives of this study were to identify genomic regions as well as genes and pathways associated with the first-lactation milk, fat, protein, and total solid yields; fat, protein, and total solid percentage; and somatic cell score (SCS) in a Thai dairy cattle population. Effects of SNPs were estimated by a weighted single-step GWAS, which back-solved the genomic breeding values predicted using single-step genomic BLUP (ssGBLUP) fitting a single-trait random regression test-day model. Genomic regions that explained at least 0.5% of the total genetic variance were selected for further analyses of candidate genes. Despite the small number of genotyped animals, genomic predictions led to an improvement in the accuracy over the traditional BLUP. Genomic predictions using weighted ssGBLUP were slightly better than the ssGBLUP. The genomic regions associated with milk production traits contained 210 candidate genes on 19 chromosomes [Bos taurus autosome (BTA) 1 to 7, 9, 11 to 16, 20 to 21, 26 to 27 and 29], whereas 21 candidate genes on 3 chromosomes (BTA 11, 16, and 21) were associated with SCS. Many genomic regions explained a small fraction of the genetic variance, indicating polygenic inheritance of the studied traits. Several candidate genes coincided with previous reports for milk production traits in Holstein cattle, especially a large region of genes on BTA14. We identified 141 and 5 novel genes related to milk production and SCS, respectively. These novel genes were also found to be functionally related to heat tolerance (e.g., SLC45A2, IRAG1, and LOC101902172), longevity (e.g., SYT10 and LOC101903327), and fertility (e.g., PAG1). These findings may be attributed to indirect selection in our population. Identified biological networks including intracellular cell transportation and protein catabolism implicate milk production, whereas the immunological pathways such as lymphocyte activation are closely related to SCS. Further studies are required to validate our findings before exploiting them in genomic selection.

Detection of whole genome selection signatures of Pakistani Teddy goat

BACKGROUND

Natural and artificial selection tend to cause variability that contributes to shape the genome of livestock in a way that differentiates them among the animal kingdom. The particular aim here is to identify positive selection signatures with whole genome pooled-sequence data of Pakistani Teddy goat.

METHODS AND RESULTS

Paired-end alignment of 635,357,043 reads of Teddy goat with (ARS1) reference genome assembly was carried out. Pooled-Heterozygosity (Hp) and Tajima's D (TD) are applied for validation and getting better hits of selection signals, while pairwise F_ST statistics is conducted on Teddy vs. Bezoar (wild goat ancestor) for genomic differentiation, moreover annotation of regions under positive selection was also performed. Hp score with - ZHp > 5 detected six windows having highest hits on Chr. 29, 9, 25, 15 and 14 that harbor HRASLS5, LACE1 and AXIN1 genes which are candidate for embryonic development, lactation and body height. Secondly, - ZTD value of > 3.3 showed 4 windows with very strong hits on Chr.5 & 9 which harbor STIM1 and ADM genes related to body mass and weight. Lastly, - ZF_ST < - 5 generated four strong signals on Chr.5 & 12 harbor LOC102183233 gene. Other significant selection signatures encompass genes associated with wool production, prolificacy and coat colors traits in this breed.

CONCLUSIONS

In brief, this study identified the genes under selection in Pakistani Teddy goat that will be helpful to refining the marker-assisted breeding policies and converging required production traits within and across other goat breeds and to explore full genetic potential of this valued species of livestock.

Genome-wide association study and pathway analysis for carcass fatness in Nellore cattle measured by ultrasound

Identifying genes or genomic regions influencing carcass-quality traits such as fatness (FTN) is essential to optimize the genetic selection processes in beef cattle. The aim of this study was to identify genomic regions associated with FTN in Nellore cattle as well as to elucidate the metabolic pathways related to the phenotypic expression. Ultrasound-based measurements of FTN were collected in 11 750 animals, with 39 903 animals in the pedigree file. Additionally, 1440 animals were genotyped using the GGP-indicus 35K SNP panel, which contained 33 623 SNPs after quality control. Twenty genes related to FTN were found on 11 chromosomes, explaining 12.96% of the total additive genetic variance. Gene ontology revealed seven genes: NR1L2, PKD2, GSK3β, EXT1, RAD51B, SORCS1 and DPH6, associated with important processes related to FTN. In addition, novel candidate genes (MAATS1, LYPD1, CDK5RAP2, RAD51B, c13H2Oorf96 and TRAPPC11) were detected and could provide further knowledge to uncover genetic regions associated to carcass fatness in beef cattle.

Genome-Wide Association Study Identifies Candidate Genes Associated with Feet and Leg Conformation Traits in Chinese Holstein Cattle

Simple Summary

Feet and leg problems are among the major reasons for dairy cows leaving the herd, as well as having direct association with production and reproduction efficiency, health (e.g., claw disorders and lameness) and welfare. Hence, understanding the genetic architecture underlying feet and conformation traits in dairy cattle offers new opportunities toward the genetic improvement and long-term selection. Through a genome-wide association study on Chinese Holstein cattle, we identified several candidate genes associated with feet and leg conformation traits. These results could provide useful information about the molecular breeding basis of feet and leg traits, thus improving the longevity and productivity of dairy cattle.

Abstract

Feet and leg conformation traits are considered one of the most important economical traits in dairy cattle and have a great impact on the profitability of milk production. Therefore, identifying the single nucleotide polymorphisms (SNPs), genes and pathways analysis associated with these traits might contribute to the genomic selection and long-term plan selection for dairy cattle. We conducted genome-wide association studies (GWASs) using the fixed and random model circulating probability unification (FarmCPU) method to identify SNPs associated with bone quality, heel depth, rear leg side view and rear leg rear view of Chinese Holstein cows. Phenotypic measurements were collected from 1000 individuals of Chinese Holstein cattle and the GeneSeek Genomic Profiler Bovine 100 K SNP chip was utilized for individual genotyping. After quality control, 984 individual cows and 84,906 SNPs remained for GWAS work; as a result, we identified 20 significant SNPs after Bonferroni correction. Several candidate genes were identified within distances of 200 kb upstream or downstream to the significant SNPs, including ADIPOR2, INPP4A, DNMT3A, ALDH1A2, PCDH7, XKR4 and CADPS. Further bioinformatics analyses showed 34 gene ontology terms and two signaling pathways were significantly enriched (p ≤ 0.05). Many terms and pathways are related to biological quality, metabolism and development processes; these identified SNPs and genes could provide useful information about the genetic architecture of feet and leg traits, thus improving the longevity and productivity of Chinese Holstein dairy cattle.

Genome-Wide Identification of Candidate Genes for Milk Production Traits in Korean Holstein Cattle

Simple Summary

Milk production traits that are economically important in the dairy industry have been considered the main selection criteria for breeding. The present genome-wide association study was performed to identify chromosomal loci and candidate genes with potential effects on milk production phenotypes in a Korean Holstein population. A total of eight significant quantitative trait locus regions were identified for milk yield (Bos taurus autosome (BTA) 7 and 14), adjusted 305-d fat yield (BTA 3, 5, and 14), adjusted 305-d protein yield (BTA 8), and somatic cell score (BTA 8 and 23) of milk production traits. Furthermore, we discovered three main candidate genes (diacylglycerol O-acyltransferase 1 (DGAT1), phosphodiesterase 4B (PDE4B), and anoctamin 2 (ANO2)) through bioinformatics analysis. These genes may help to understand better the underlying genetic and molecular mechanisms for milk production phenotypes in the Korean Holstein population.

Abstract

We performed a genome-wide association study and fine mapping using two methods (single marker regression: frequentist approach and Bayesian C (BayesC): fitting selected single nucleotide polymorphisms (SNPs) in a Bayesian framework) through three high-density SNP chip platforms to analyze milk production phenotypes in Korean Holstein cattle (n = 2780). We identified four significant SNPs for each phenotype in the single marker regression model: AX-311625843 and AX-115099068 on Bos taurus autosome (BTA) 14 for milk yield (MY) and adjusted 305-d fat yield (FY), respectively, AX-428357234 on BTA 18 for adjusted 305-d protein yield (PY), and AX-185120896 on BTA 5 for somatic cell score (SCS). Using the BayesC model, we discovered significant 1-Mb window regions that harbored over 0.5% of the additive genetic variance effects for four milk production phenotypes. The concordant significant SNPs and 1-Mb window regions were characterized into quantitative trait loci (QTL). Among the QTL regions, we focused on a well-known gene (diacylglycerol O-acyltransferase 1 (DGAT1)) and newly identified genes (phosphodiesterase 4B (PDE4B), and anoctamin 2 (ANO2)) for MY and FY, and observed that DGAT1 is involved in glycerolipid metabolism, fat digestion and absorption, metabolic pathways, and retinol metabolism, and PDE4B is involved in cAMP signaling. Our findings suggest that the candidate genes in QTL are strongly related to physiological mechanisms related to the fat production and consequent total MY in Korean Holstein cattle.

Direct Phenotyping and Principal Component Analysis of Type Traits Implicate Novel QTL in Bovine Mastitis through Genome-Wide Association

Simple Summary

It is well established that the physical conformation of a cow’s udder and teats may influence her susceptibility to mastitis, an inflammatory condition of the udder, which has 25% prevalence in the United States. Our aim was to improve the biological understanding of the genetics underlying mastitis by intensively characterizing cows for udder and teat conformation, including the novel traits of teat width and end shape, and directly associating those phenotypes with high-density genotypes for those exact same cows. We also generated a composite measure that accounts for multiple high-mastitis-risk udder and teat conformations in a single index for risk phenotypes. Using this approach, we identified novel genetic markers associated with udder and teat conformation, which may be good candidates for inclusion in national genetic evaluations for selection of mastitis-resistant cows. Mastitis is the costliest disease facing US dairy producers, and integrating genetic information regarding disease susceptibility into breeding programs may be an efficient way to mitigate economic loss, support the judicious use of antimicrobials, and improve animal welfare.

Abstract

Our objectives were to robustly characterize a cohort of Holstein cows for udder and teat type traits and perform high-density genome-wide association studies for those traits within the same group of animals, thereby improving the accuracy of the phenotypic measurements and genomic association study. Additionally, we sought to identify a novel udder and teat trait composite risk index to determine loci with potential pleiotropic effects related to mastitis. This approach was aimed at improving the biological understanding of the genetic factors influencing mastitis. Cows (N = 471) were genotyped on the Illumina BovineHD777k beadchip and scored for front and rear teat length, width, end shape, and placement; fore udder attachment; udder cleft; udder depth; rear udder height; and rear udder width. We used principal component analysis to create a single composite measure describing type traits previously linked to high odds of developing mastitis within our cohort of cows. Genome-wide associations were performed, and 28 genomic regions were significantly associated (Bonferroni-corrected p < 0.05). Interrogation of these genomic regions revealed a number of biologically plausible genes whicht may contribute to the development of mastitis and whose functions range from regulating cell proliferation to immune system signaling, including ZNF683, DHX9, CUX1, TNNT1, and SPRY1. Genetic investigation of the risk composite trait implicated a novel locus and candidate genes that have potentially pleiotropic effects related to mastitis.

Identification of Genomic Regions Associated with Concentrations of Milk Fat, Protein, Urea and Efficiency of Crude Protein Utilization in Grazing Dairy Cows

The objective of this study was to identify genomic regions associated with milk fat percentage (FP), crude protein percentage (CPP), urea concentration (MU) and efficiency of crude protein utilization (ECPU: ratio between crude protein yield in milk and dietary crude protein intake) using grazing, mixed-breed, dairy cows in New Zealand. Phenotypes from 634 Holstein Friesian, Jersey or crossbred cows were obtained from two herds at Massey University. A subset of 490 of these cows was genotyped using Bovine Illumina 50K SNP-chips. Two genome-wise association approaches were used, a single-locus model fitted to data from 490 cows and a single-step Bayes C model fitted to data from all 634 cows. The single-locus analysis was performed with the Efficient Mixed-Model Association eXpedited model as implemented in the SVS package. Single nucleotide polymorphisms (SNPs) with genome-wide association p-values ≤ 1.11 × 10⁻⁶ were considered as putative quantitative trait loci (QTL). The Bayes C analysis was performed with the JWAS package and 1-Mb genomic windows containing SNPs that explained > 0.37% of the genetic variance were considered as putative QTL. Candidate genes within 100 kb from the identified SNPs in single-locus GWAS or the 1-Mb windows were identified using gene ontology, as implemented in the Ensembl Genome Browser. The genes detected in association with FP (MGST1, DGAT1, CEBPD, SLC52A2, GPAT4, and ACOX3) and CPP (DGAT1, CSN1S1, GOSR2, HERC6, and IGF1R) were identified as candidates. Gene ontology revealed six novel candidate genes (GMDS, E2F7, SIAH1, SLC24A4, LGMN, and ASS1) significantly associated with MU whose functions were in protein catabolism, urea cycle, ion transportation and N excretion. One novel candidate gene was identified in association with ECPU (MAP3K1) that is involved in post-transcriptional modification of proteins. The findings should be validated using a larger population of New Zealand grazing dairy cows.

Structural equation modeling for unraveling the multivariate genomic architecture of milk proteins in dairy cattle

The aims of this study were to investigate potential functional relationships among milk protein fractions in dairy cattle and to carry out a structural equation model (SEM) GWAS to provide a decomposition of total SNP effects into direct effects and effects mediated by traits that are upstream in a phenotypic network. To achieve these aims, we first fitted a mixed Bayesian multitrait genomic model to infer the genomic correlations among 6 milk nitrogen fractions [4 caseins (CN), namely κ-, β-, α_S1-, and α_S2-CN, and 2 whey proteins, namely β-lactoglobulin (β-LG) and α-lactalbumin (α-LA)], in a population of 989 Italian Brown Swiss cows. Animals were genotyped with the Illumina BovineSNP50 Bead Chip v.2 (Illumina Inc.). A Bayesian network approach using the max-min hill-climbing (MMHC) algorithm was implemented to model the dependencies or independence among traits. Strong and negative genomic correlations were found between β-CN and α_S1-CN (-0.706) and between β-CN and κ-CN (-0.735). The application of the MMHC algorithm revealed that κ-CN and β-CN seemed to directly or indirectly influence all other milk protein fractions. By integrating multitrait model GWAS and SEM-GWAS, we identified a total of 127 significant SNP for κ-CN, 89 SNP for β-CN, 30 SNP for α_S1-CN, and 14 SNP for α_S2-CN (mostly shared among CN and located on Bos taurus autosome 6) and 15 SNP for β-LG (mostly located on Bos taurus autosome 11), whereas no SNP passed the significance threshold for α-LA. For the significant SNP, we assessed and quantified the contribution of direct and indirect paths to total marker effect. Pathway analyses confirmed that common regulatory mechanisms (e.g., energy metabolism and hormonal and neural signals) are involved in the control of milk protein synthesis and metabolism. The information acquired might be leveraged for setting up optimal management and selection strategies aimed at improving milk quality and technological characteristics in dairy cattle.

The distribution of runs of homozygosity in the genome of river and swamp buffaloes reveals a history of adaptation, migration and crossbred events

Background

Water buffalo is one of the most important livestock species in the world. Two types of water buffalo exist: river buffalo (Bubalus bubalis bubalis) and swamp buffalo (Bubalus bubalis carabanensis). The buffalo genome has been recently sequenced, and thus a new 90 K single nucleotide polymorphism (SNP) bead chip has been developed. In this study, we investigated the genomic population structure and the level of inbreeding of 185 river and 153 swamp buffaloes using runs of homozygosity (ROH). Analyses were carried out jointly and separately for the two buffalo types.

Results

The SNP bead chip detected in swamp about one-third of the SNPs identified in the river type. In total, 18,116 ROH were detected in the combined data set (17,784 SNPs), and 16,251 of these were unique. ROH were present in both buffalo types mostly detected (~ 59%) in swamp buffalo. The number of ROH per animal was larger and genomic inbreeding was higher in swamp than river buffalo. In the separated datasets (46,891 and 17,690 SNPs for river and swamp type, respectively), 19,760 and 10,581 ROH were found in river and swamp, respectively. The genes that map to the ROH islands are associated with the adaptation to the environment, fitness traits and reproduction.

Conclusions

Analysis of ROH features in the genome of the two water buffalo types allowed their genomic characterization and highlighted differences between buffalo types and between breeds. A large ROH island on chromosome 2 was shared between river and swamp buffaloes and contained genes that are involved in environmental adaptation and reproduction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00616-3.